The present invention relates generally to the management of assets, and more particularly to the reconciliation of physical and financial asset information and aggregate forecasting of information for reconciliation exceptions.
Large organizations often have trouble keeping track of their assets as the assets are purchased and deployed throughout the organization. Proper accounting and tracking of these assets is necessary to ensure compliance with arrangements such as leases and software licenses, and to ensure there are no material discrepancies between the physical existence of these assets and what is recorded on the financial set of books.
For example, physical assets such as laptop computers and portable electronic devices (which are often prone to loss or theft) are typically accounted for not only in physical inventory but also as financial assets of a company for accounting, auditing, and other financial purposes. Further complicating matters is the fact that organizations can be very large and thus can have large numbers of asset reconciliation exceptions across an enterprise. Often times an organization will rely on the information that is stored in a financial system to track and manage their assets; however this rarely reflects the real world of assets that are actually deployed within the organization. The financial information lacks the impact of events such as operational asset disposal, unrecorded sales, theft, etc.
Increasingly, organizations are deploying asset tracking (physical discovery) mechanisms that can retrieve the actual asset information as the asset is utilized in the organization. Organizations then take the information that comes from the physical discovery and reconcile that information back to the financial system. In one current approach, organizations utilize software packages from various “discovery” vendors. The discovery vendor software is typically installed on, or pushed onto, information technology (IT) devices such as servers, desktops, or laptops. The discovery software can then perform an inventory scan of devices across an enterprise and reports back device information which can include such data as the device manufacturer, model, serial number, etc. The discovery software also can report back on the various software installed and/or activated on the device. The reported data thus can be used to determine the number and type of each asset across the enterprise.
This data can then be used with a product such as PeopleSoft IT Asset Management (ITAM), available from Oracle Corporation of Redwood Shores, Calif., which integrates data from third party discovery vendors, where the vendor solutions discover and take inventory of intelligent IT devices connected to an organization's network. The information obtained includes details and/or attributes about each IT device, such as the manufacturer, model, machine name, installed software, and serial number. A physical count of the number of IT devices, for example, then can be compared with information stored in an asset repository containing financial asset information. Typically, this involves doing manual queries and then manually creating reports in spreadsheet applications in order to determine where discrepancies might exist between the physical asset information and the financial asset information. Further, determining financial impact information for these discrepancies is an even more arduous manual task.
Currently, there is no easy way to determine where such exceptions exist, forecast the aggregate financial impact of those exceptions, and allow the exceptions to be resolved such as by locating, updating, or retiring the asset(s).